Cargando…
DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data
Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for i...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179940/ https://www.ncbi.nlm.nih.gov/pubmed/32282793 http://dx.doi.org/10.1371/journal.pcbi.1007522 |
_version_ | 1783525732396302336 |
---|---|
author | Jiang, Yi Giase, Gina Grennan, Kay Shieh, Annie W. Xia, Yan Han, Lide Wang, Quan Wei, Qiang Chen, Rui Liu, Sihan White, Kevin P. Chen, Chao Li, Bingshan Liu, Chunyu |
author_facet | Jiang, Yi Giase, Gina Grennan, Kay Shieh, Annie W. Xia, Yan Han, Lide Wang, Quan Wei, Qiang Chen, Rui Liu, Sihan White, Kevin P. Chen, Chao Li, Bingshan Liu, Chunyu |
author_sort | Jiang, Yi |
collection | PubMed |
description | Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS. |
format | Online Article Text |
id | pubmed-7179940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-71799402020-05-05 DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data Jiang, Yi Giase, Gina Grennan, Kay Shieh, Annie W. Xia, Yan Han, Lide Wang, Quan Wei, Qiang Chen, Rui Liu, Sihan White, Kevin P. Chen, Chao Li, Bingshan Liu, Chunyu PLoS Comput Biol Research Article Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS. Public Library of Science 2020-04-13 /pmc/articles/PMC7179940/ /pubmed/32282793 http://dx.doi.org/10.1371/journal.pcbi.1007522 Text en © 2020 Jiang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Jiang, Yi Giase, Gina Grennan, Kay Shieh, Annie W. Xia, Yan Han, Lide Wang, Quan Wei, Qiang Chen, Rui Liu, Sihan White, Kevin P. Chen, Chao Li, Bingshan Liu, Chunyu DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title | DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title_full | DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title_fullStr | DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title_full_unstemmed | DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title_short | DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
title_sort | drams: a tool to detect and re-align mixed-up samples for integrative studies of multi-omics data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7179940/ https://www.ncbi.nlm.nih.gov/pubmed/32282793 http://dx.doi.org/10.1371/journal.pcbi.1007522 |
work_keys_str_mv | AT jiangyi dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT giasegina dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT grennankay dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT shiehanniew dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT xiayan dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT hanlide dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT wangquan dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT weiqiang dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT chenrui dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT liusihan dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT whitekevinp dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT chenchao dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT libingshan dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata AT liuchunyu dramsatooltodetectandrealignmixedupsamplesforintegrativestudiesofmultiomicsdata |